Akshaykumarcp/numpy

Numpy for data science

Table of content

1. What is numpy ❓
2. What numpy provides ❓
3. Numpy Operations 👨‍💻
4. Numpy MindMap 🧠
5. Connect with me 😃

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1. What is numpy ❓

• The fundamental package for scientific computing with Python

• Efficient interface to store and manipulate data

• Core of almost all the data science tools in python

• Can create and manipulate multidimensional arrays and perform numerical operations at high speed

• Numpy has many built in functions for linear algebra, statistical analysis, transformations etc

• Libraries such as keras, pandas, matplotlib depend on numpy lib

• Data is mostly stored in arrays before fed to the ML algo

• Official github repo of numpy can be found here

  Numpy 
  
  Num --> Numeric
  py --> python
  
  Numpy = numerical python
  

• Problem with python lists

  • Objects are structures with more info
  • List of single object type have redundant info
  • Flexible but inefficient

2. Numpy provides 🔢

• Good efficiency because closer to hardware
• Arrays and multi-dimention arrays
  • Fixed type
  • Efficient
  • Less flexible as compared to lists
• Scientific computation
• Memory & time efficient

3. Numpy Operations 👨‍💻

• 3.1 Create array
• 3.2 Common arrays
• 3.3 Data types
• 3.4 Indexing
• 3.5 Slicing
• 3.6 Element wise operations
• 3.7 Math functions
• 3.8 Reductions
• 3.9 Statistics
• 3.10 Case study
• 3.11 Broadcasting
• 3.12 Array shape manipulation
• 3.13 Add dimentions

4. Numpy MindMap 🧠

5. Connect with me 😃